def annotation_color(cmap):
cmap_color = mpl.colormaps[cmap]
return cmap_color(0.85)
def image_orientation(func):
@wraps(func)
def wrapper(*args, **kwargs):
Left, Right, Anterior, Posterior, Head, Feet = "L", "R", "A", "P", "H", "F"
axial_plane = [Anterior, Right, Posterior, Left]
coronal_plane = [Right, Head, Left, Feet]
sagittal_plane = [Anterior, Head, Posterior, Feet]
plane = func(*args, **kwargs)
# Patient Position (0018,5100) specifies the position of the patient relative to
# the space of the imaging device and is used for annotation purposes only,
# but does not provide a precise mathematical relationship between the patient and
# the imaging device
patient_position = dicom_file.PatientPosition
if plane == "Axial":
term_list = ["S", "DR", "P", "DL"]
top = 4 - term_list.index(patient_position[2:])
if patient_position.startswith("HF"):
right = top + 1
left = right + 2
else:
left = top + 1
right = left + 2
bottom = top + 2
top, right, bottom, left = [
axial_plane[index] if index < 4 else axial_plane[index - 4]
for index in [top, right, bottom, left]
]
elif plane == "Coronal":
term_list = ["DL", "DR"]
top = term_list.index(patient_position[2:]) * 2
if patient_position.startswith("AF"):
right = top + 1
left = right + 2
else:
left = top + 1
right = left + 2
bottom = top + 2
top, right, bottom, left = [
coronal_plane[index] if index < 4 else coronal_plane[index - 4]
for index in [top, right, bottom, left]
]
elif plane == "Sagittal":
term_list = ["LF", "RF"]
if patient_position.endswith("S"):
top = 0
right = term_list.index(patient_position[:2]) * 2 + 1
left = right + 2
else:
top = 2
left = term_list.index(patient_position[:2]) * 2 + 1
right = left + 2
bottom = top + 2
top, right, bottom, left = [
sagittal_plane[index] if index < 4 else sagittal_plane[index - 4]
for index in [top, right, bottom, left]
]
else:
top, right, bottom, left = np.repeat("", 4)
return plane, top, right, bottom, left
return wrapper
def orientation_annotator(func):
@wraps(func)
def wrapper(ax, cmap, dicom_file, fontsize_adjustment):
plane, top, right, bottom, left = func(dicom_file)
ax.text(
0.4875,
0.95,
top,
transform=ax.transAxes,
fontweight="heavy",
fontsize=11 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
ax.text(
0.4875,
0.025,
bottom,
transform=ax.transAxes,
fontweight="heavy",
fontsize=11 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
ax.text(
0.95,
0.4875,
right,
transform=ax.transAxes,
fontweight="heavy",
fontsize=11 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
ax.text(
0.025,
0.4875,
left,
transform=ax.transAxes,
fontweight="heavy",
fontsize=11 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
return plane
return wrapper
def first_max(list):
max_element = list[0]
for element in list[1:]:
if len(element) > len(max_element):
max_element = element
return max_element
def text_aligner(text):
line_list = text.split("\n")
max_length = len(first_max(line_list))
line_list = [
(":" + " " * (max_length - len(line))).join(line.split(":"))
if len(line) < max_length
else line
for line in line_list
]
text = "\n".join(line_list)
return text
def plane_information_annotator(func):
@wraps(func)
def wrapper(ax, cmap, dicom_file, fontsize_adjustment):
plane = func(ax, cmap, dicom_file, fontsize_adjustment)
text = f"Image: {dicom_file.Columns} x {dicom_file.Rows}"
text += f"\nImage Plane: {plane}"
text += f"\nPatient Position: {dicom_file.PatientPosition}"
text += f"\nSlice Thickness: {dicom_file.SliceThickness:.1f} mm"
text += f"\nSlice Location: {dicom_file.SliceLocation:.1f} mm"
ax.text(
0.575,
0.825,
text_aligner(text),
transform=ax.transAxes,
fontsize=9 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
return wrapper
@plane_information_annotator
@orientation_annotator
@image_orientation
def get_plane_information(dicom_file):
# The DICOM standard defines a patient-oriented reference coordinate system (RCS) that
# enables the user to measure the position and orientation of the image relative to
# the patient
# Image Orientation (Patient) (0020,0037) specifies the cosine of the orientation of
# the first row and column relative to the patient, which should be provided as a pair,
# where the row values for the x, y, and z axes are followed by the column values for
# the x, y, and z axes
# The orientation of the axes is determined solely by the orientation of the patient, i.e.,
# the RCS and Image Orientation (Patient) (0020,0037) values specify the orientation of
# the image frame rows and columns
IOP = dicom_file.ImageOrientationPatient
row_xyz = IOP[:3]
column_xyz = IOP[3:]
# `numpy.cross` returns the cross product of two vector arrays
# The geometric interpretation of the cross product is that two vectors determine a plane,
# and the cross product points in a direction different from both vectors
# The Patient-Based Coordinate System is a right handed system, i.e., the vector
# cross product of a unit vector along the positive x-axis and a unit vector along
# the positive y-axis is equal to a unit vector along the positive z-axis
plane = [abs(round(x)) for x in np.cross(row_xyz, column_xyz)]
# The Image Type (0008,0008) identifies important image identification features
# and it returns the Pixel Data Characteristics, Patient Examination Characteristics,
# Modality Specific Characteristics, and Implementation Specific Identifiers
# While the third value (Modality Specific Characteristics) should identify any
# specialization specific to the Image Information Object Definition (IOD), it
# and the values that follow it are not mandatory, unlike the first two values,
# and therefore some DICOM data does not have this value
if plane[0] == 1 and plane[1] == 0 and plane[2] == 0:
return "Sagittal"
elif plane[0] == 0 and plane[1] == 1 and plane[2] == 0:
return "Coronal"
elif plane[0] == 0 and plane[1] == 0 and plane[2] == 1:
return "Axial"
else:
"Unknown"
def get_patient_information(ax, cmap, dicom_file, fontsize_adjustment, patien_age):
text = f"Patient ID: {dicom_file.PatientID}"
text += f"\nPatient's Sex: {dicom_file.PatientSex}"
try:
patien_age = dicom_file.PatientAge.strip(str(0))[:-1]
except AttributeError:
patien_age = patien_age
text += f"\nPatient's Age: {patien_age} y"
text += f"\nModality: {dicom_file.Modality}"
text += f"\nBody Part Examined: {dicom_file.BodyPartExamined}"
ax.text(
0.04,
0.825,
text_aligner(text),
transform=ax.transAxes,
fontsize=9 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
def get_date_information(ax, cmap, dicom_file, fontsize_adjustment):
date = datetime.strptime(dicom_file.StudyDate,
"%Y%m%d").strftime("%Y.%m.%d")
text = f"Study Date: {date}"
ax.text(
0.04,
0.05,
text,
transform=ax.transAxes,
fontsize=7 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
def get_manufacturer_information(ax, cmap, dicom_file, fontsize_adjustment):
text = f"Manufacturer: {dicom_file.Manufacturer}"
ax.text(
0.525,
0.05,
text.rjust(36),
transform=ax.transAxes,
fontsize=7 + fontsize_adjustment,
fontfamily="monospace",
c=annotation_color(cmap),
)
def DICOM_2D_image(
dicom_file, ax, title, cmap, fontsize_adjustment=0, patien_age="xxx"
):
ax.imshow(dicom_file.pixel_array, cmap)
ax.grid(False)
ax.set_title(title, loc="center", pad=10)
ax.set(xticks=[], yticks=[], frame_on=False)
get_patient_information(ax, cmap, dicom_file,
fontsize_adjustment, patien_age)
get_plane_information(ax, cmap, dicom_file, fontsize_adjustment)
get_date_information(ax, cmap, dicom_file, fontsize_adjustment)
get_manufacturer_information(ax, cmap, dicom_file, fontsize_adjustment)
plt.rcParams["figure.figsize"] = (
figure_size[0] / 3 * 2, figure_size[1] / 4 * 5)
fig, axs = plt.subplots(nrows=2, ncols=2)
DICOM_2D_image(
dicom_file,
axs[0, 0],
f"{dicom_file.BodyPartExamined.capitalize()} CT medical image displayed using "
"colormap 'gray'",
cmap="gray",
)
DICOM_2D_image(
dicom_file,
axs[0, 1],
f"{dicom_file.BodyPartExamined.capitalize()} CT medical image displayed using "
"colormap 'bone'",
cmap="bone",
)
DICOM_2D_image(
random_dicom_file,
axs[1, 0],
f"{random_dicom_file.BodyPartExamined.capitalize()} CT medical image displayed using "
"colormap 'gray'",
cmap="gray",
)
DICOM_2D_image(
random_dicom_file,
axs[1, 1],
f"{random_dicom_file.BodyPartExamined.capitalize()} CT medical image displayed using "
"colormap 'bone'",
cmap="bone",
)
fig.suptitle(
"Visualization of CT Medical Image Display with Supplementary Information",
fontsize="x-large",
x=0.5,
y=0,
)
plt.tight_layout()
plt.show()
